We’re using machine learning for quality assurance and leverage advanced Bayesian statistics to ensure the highest possible data quality.
Accidental or inattentive clicks can happen - but they shouldn’t make it into the dataset. We’ve developed an unsupervised learning model based on dozens of active and passive data points to identify and filter out unreliable responses in our advanced data processing.
Partial responses can contribute valuable insights and improve the overall accuracy of the dataset, for example, by highlighting links between gender and brand awareness. We use partial completions to check and ensure that all correlations are as accurate as possible during our advanced data processing.
We enhance precision through Bayesian time series modelling. For each brand, we feed multiple waves of data into our probabilistic models, employing regression and poststratification techniques to stabilise KPIs and correct for potential sample composition effects. This method ensures accurate estimates even for niche segments, leading to up to 90% lower margins of error compared to industry standards.
Discover how Latana's advanced data collection and analysis techniques can elevate your brand strategy. From comprehensive surveys to sophisticated algorithms, our platform provides the clarity you need to make confident, data-driven decisions.
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